Evaluation against your data
We benchmark candidate models on your actual workload — not synthetic test sets. You see the numbers before any commitment.
Six models. One mission: compact AI that actually ships.
Each model is purpose-built for a specific task domain. Smaller, faster, more accurate on the work that matters — at a fraction of the cost of general-purpose models.
From research to your production stack.
Most teams underestimate the gap between a model that works in a notebook and one that runs in production. We close it.
Fine-tuning to your domain
We adapt the architecture to your edge cases, your taxonomy, and your failure modes. The model learns the work that matters to you.
Quantization without accuracy loss
We compress the model so it runs on hardware you already own. Single-GPU inference, predictable latency, no specialized infra.
Deployment to your environment
Self-hosted, VPC, or air-gapped — we ship to the environment your security team already approved. Your data never leaves your network.
Drift monitoring and retraining
Production AI degrades silently. We watch for it, retrain on schedule, and a named engineer answers when something looks off.
Why compact models win.
Bigger isn’t smarter. The right model for the task beats a general model on every metric that matters in production.
01
Domain-Specialized
Compact models trained on your task outperform general models 10–30x larger on the metrics that decide whether the system ships.
02
Run on Your Hardware
All ≤7B parameters. Single-GPU inference. No specialized infrastructure, no frontier-scale latency, no cloud lock-in.
03
Predictable Cost
$0.03 per 1M tokens, flat. No per-call surprises, no provider price hikes, no rate-limit cliffs at the worst possible moment.
04
Accuracy You Can Audit
Open evaluation methodology, reproducible benchmarks, every claim backed by a test you can rerun. No vendor magic.
05
Yours to Own
Weights, fine-tunes, and deployment artifacts transfer to you on delivery. No vendor lock-in, no escrow drama if we ever go away.
Engagement clarity
What you get versus the alternatives.
Particula Models
Trained for one task, validated against yours
≤7B params, single-GPU inference
$0.03 / 1M tokens, flat
Self-hosted or VPC, your data never leaves
Open eval methodology, reproducible benchmarks
General LLM API
One model, all tasks — average at everything
Frontier-scale infra, frontier-scale latency
$1–75 / 1M tokens, billed per call
Your data trains their next model
Closed evals, vendor-published numbers
Build in-house
12+ months to hire and ship a first model
Burnout risk on a single critical hire
Capex on GPUs before you know the model works
No outside view on what’s actually feasible
Years of compounding ML platform debt
Why we build small
Frontier models are extraordinary, but they’re the wrong tool for most production work. A 7B model trained on your task will outperform a 175B general model on every metric that ships your product. We’ve shipped enough of these to know.
Sebastian MondragonFounder, Particula Tech
Frequently asked
On a narrow, well-scoped task: almost always — and at 30–100x lower cost. We benchmark against your real data on the eval call, so you don’t take our word for it.
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